Data integration and batch correction¶

An important task of single-cell analysis is the integration of several samples, which we can perform with omicverse.

Here we demonstrate how to merge data using omicverse and perform a corrective analysis for batch effects. We provide a total of 4 methods for batch effect correction in omicverse, including harmony, scanorama and combat which do not require GPU, and SIMBA which requires GPU. if available, we recommend using GPU-based scVI and scANVI to get the best batch effect correction results.

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import omicverse as ov
#print(f"omicverse version: {ov.__version__}")
import scanpy as sc
#print(f"scanpy version: {sc.__version__}")
ov.utils.ov_plot_set()
import omicverse as ov
#print(f"omicverse version: {ov.__version__}")
import scanpy as sc
#print(f"scanpy version: {sc.__version__}")
ov.utils.ov_plot_set()

   ____            _     _    __                  
  / __ \____ ___  (_)___| |  / /__  _____________ 
 / / / / __ `__ \/ / ___/ | / / _ \/ ___/ ___/ _ \ 
/ /_/ / / / / / / / /__ | |/ /  __/ /  (__  )  __/ 
\____/_/ /_/ /_/_/\___/ |___/\___/_/  /____/\___/                                              

Version: 1.5.3, Tutorials: https://omicverse.readthedocs.io/

Data integration¶

First, we need to concat the data of scRNA-seq from different batch. We can use sc.concat to perform it。

The dataset we will use to demonstrate data integration contains several samples of bone marrow mononuclear cells. These samples were originally created for the Open Problems in Single-Cell Analysis NeurIPS Competition 2021.

We selected sample of s1d3, s2d1 and s3d7 to perform integrate. The individual data can be downloaded from figshare.

s1d3:
s2d1:
s3d7:

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adata1=ov.read('neurips2021_s1d3.h5ad')
adata1.obs['batch']='s1d3'
adata2=ov.read('neurips2021_s2d1.h5ad')
adata2.obs['batch']='s2d1'
adata3=ov.read('neurips2021_s3d7.h5ad')
adata3.obs['batch']='s3d7'
adata1=ov.read('neurips2021_s1d3.h5ad')
adata1.obs['batch']='s1d3'
adata2=ov.read('neurips2021_s2d1.h5ad')
adata2.obs['batch']='s2d1'
adata3=ov.read('neurips2021_s3d7.h5ad')
adata3.obs['batch']='s3d7'

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adata=sc.concat([adata1,adata2,adata3],merge='same')
adata
adata=sc.concat([adata1,adata2,adata3],merge='same')
adata

Out[3]:

AnnData object with n_obs × n_vars = 27423 × 13953
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train'
    var: 'feature_types', 'gene_id'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap'
    layers: 'counts'

We can see that there are now three elements in the batch

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adata.obs['batch'].unique()
adata.obs['batch'].unique()

Out[4]:

array(['s1d3', 's2d1', 's3d7'], dtype=object)

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import numpy as np
adata.X=adata.X.astype(np.int64)
import numpy as np
adata.X=adata.X.astype(np.int64)

Data preprocess and Batch visualize¶

We first performed quality control of the data and normalisation with screening for highly variable genes. Then visualise potential batch effects in the data.

Here, we can set batch_key=batch to correct the doublet detectation and Highly variable genes identifcation.

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adata=ov.pp.qc(adata,
              tresh={'mito_perc': 0.2, 'nUMIs': 500, 'detected_genes': 250},
              batch_key='batch')
adata
adata=ov.pp.qc(adata,
              tresh={'mito_perc': 0.2, 'nUMIs': 500, 'detected_genes': 250},
              batch_key='batch')
adata

Calculate QC metrics
End calculation of QC metrics.
Original cell number: 27423
Begin of post doublets removal and QC plot
Running Scrublet
filtered out 116 genes that are detected in less than 3 cells
normalizing counts per cell
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
Embedding transcriptomes using PCA...
Automatically set threshold at doublet score = 0.52
Detected doublet rate = 0.0%
Estimated detectable doublet fraction = 11.5%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 0.3%
filtered out 26 genes that are detected in less than 3 cells
normalizing counts per cell
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
Embedding transcriptomes using PCA...
Automatically set threshold at doublet score = 0.50
Detected doublet rate = 0.0%
Estimated detectable doublet fraction = 36.7%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 0.0%
filtered out 11 genes that are detected in less than 3 cells
normalizing counts per cell
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
Embedding transcriptomes using PCA...
Automatically set threshold at doublet score = 0.52
Detected doublet rate = 0.1%
Estimated detectable doublet fraction = 21.3%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 0.3%
    Scrublet finished (0:00:20)
Cells retained after scrublet: 27412, 11 removed.
End of post doublets removal and QC plots.
Filters application (seurat or mads)
Lower treshold, nUMIs: 500; filtered-out-cells: 0
Lower treshold, n genes: 250; filtered-out-cells: 420
Lower treshold, mito %: 0.2; filtered-out-cells: 285
Filters applicated.
Total cell filtered out with this last --mode seurat QC (and its chosen options): 705
Cells retained after scrublet and seurat filtering: 26707, 716 removed.

Out[8]:

AnnData object with n_obs × n_vars = 26707 × 13953
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'nUMIs', 'mito_perc', 'detected_genes', 'cell_complexity', 'n_genes', 'doublet_score', 'predicted_doublet', 'passing_mt', 'passing_nUMIs', 'passing_ngenes'
    var: 'feature_types', 'gene_id', 'mt', 'n_cells'
    uns: 'scrublet'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap'
    layers: 'counts'

We can store the raw counts if we need the raw counts after filtered the HVGs.

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ov.utils.store_layers(adata,layers='counts')
adata
ov.utils.store_layers(adata,layers='counts')
adata

......The X of adata have been stored in counts

Out[9]:

AnnData object with n_obs × n_vars = 26707 × 13953
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'nUMIs', 'mito_perc', 'detected_genes', 'cell_complexity', 'n_genes', 'doublet_score', 'predicted_doublet', 'passing_mt', 'passing_nUMIs', 'passing_ngenes'
    var: 'feature_types', 'gene_id', 'mt', 'n_cells'
    uns: 'scrublet', 'layers_counts'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap'
    layers: 'counts'

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adata=ov.pp.preprocess(adata,mode='shiftlog|pearson',
                       n_HVGs=3000,batch_key=None)
adata
adata=ov.pp.preprocess(adata,mode='shiftlog|pearson',
                       n_HVGs=3000,batch_key=None)
adata

Begin robust gene identification
After filtration, 13953/13953 genes are kept. Among 13953 genes, 13953 genes are robust.
End of robust gene identification.
Begin size normalization: shiftlog and HVGs selection pearson
normalizing counts per cell The following highly-expressed genes are not considered during normalization factor computation:
['IGKC', 'HBB', 'MALAT1', 'IGHA1', 'IGHM', 'HBA2', 'IGLC1', 'IGLC2', 'IGLC3']
    finished (0:00:00)
extracting highly variable genes
--> added
    'highly_variable', boolean vector (adata.var)
    'highly_variable_rank', float vector (adata.var)
    'highly_variable_nbatches', int vector (adata.var)
    'highly_variable_intersection', boolean vector (adata.var)
    'means', float vector (adata.var)
    'variances', float vector (adata.var)
    'residual_variances', float vector (adata.var)
End of size normalization: shiftlog and HVGs selection pearson

Out[10]:

AnnData object with n_obs × n_vars = 26707 × 13953
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'nUMIs', 'mito_perc', 'detected_genes', 'cell_complexity', 'n_genes', 'doublet_score', 'predicted_doublet', 'passing_mt', 'passing_nUMIs', 'passing_ngenes'
    var: 'feature_types', 'gene_id', 'mt', 'n_cells', 'percent_cells', 'robust', 'mean', 'var', 'residual_variances', 'highly_variable_rank', 'highly_variable_features'
    uns: 'scrublet', 'layers_counts', 'log1p', 'hvg'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap'
    layers: 'counts'

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adata.raw = adata
adata = adata[:, adata.var.highly_variable_features]
adata
adata.raw = adata
adata = adata[:, adata.var.highly_variable_features]
adata

Out[11]:

View of AnnData object with n_obs × n_vars = 26707 × 3000
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'nUMIs', 'mito_perc', 'detected_genes', 'cell_complexity', 'n_genes', 'doublet_score', 'predicted_doublet', 'passing_mt', 'passing_nUMIs', 'passing_ngenes'
    var: 'feature_types', 'gene_id', 'mt', 'n_cells', 'percent_cells', 'robust', 'mean', 'var', 'residual_variances', 'highly_variable_rank', 'highly_variable_features'
    uns: 'scrublet', 'layers_counts', 'log1p', 'hvg'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap'
    layers: 'counts'

We can save the pre-processed data.

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adata.write_h5ad('neurips2021_batch_normlog.h5ad',compression='gzip')
adata.write_h5ad('neurips2021_batch_normlog.h5ad',compression='gzip')

Similarly, we calculated PCA for HVGs and visualised potential batch effects in the data using pymde. pymde is GPU-accelerated UMAP.

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ov.pp.scale(adata)
ov.pp.pca(adata,layer='scaled',n_pcs=50)

adata.obsm["X_mde_pca"] = ov.utils.mde(adata.obsm["scaled|original|X_pca"])
ov.pp.scale(adata)
ov.pp.pca(adata,layer='scaled',n_pcs=50)

adata.obsm["X_mde_pca"] = ov.utils.mde(adata.obsm["scaled|original|X_pca"])

... as `zero_center=True`, sparse input is densified and may lead to large memory consumption

There is a very clear batch effect in the data

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ov.utils.embedding(adata,
                basis='X_mde_pca',frameon='small',
                color=['batch','cell_type'],show=False)
ov.utils.embedding(adata,
                basis='X_mde_pca',frameon='small',
                color=['batch','cell_type'],show=False)

Out[14]:

[<AxesSubplot: title={'center': 'batch'}, xlabel='X_mde_pca1', ylabel='X_mde_pca2'>,
 <AxesSubplot: title={'center': 'cell_type'}, xlabel='X_mde_pca1', ylabel='X_mde_pca2'>]

Harmony¶

Harmony is an algorithm for performing integration of single cell genomics datasets. Please check out manuscript on Nature Methods.

The function ov.single.batch_correction can be set in three methods: harmony,combat and scanorama

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adata_harmony=ov.single.batch_correction(adata,batch_key='batch',
                                        methods='harmony',n_pcs=50)
adata
adata_harmony=ov.single.batch_correction(adata,batch_key='batch',
                                        methods='harmony',n_pcs=50)
adata

...Begin using harmony to correct batch effect
... as `zero_center=True`, sparse input is densified and may lead to large memory consumption

2023-11-19 20:25:03,351 - harmonypy - INFO - Computing initial centroids with sklearn.KMeans...
INFO:harmonypy:Computing initial centroids with sklearn.KMeans...
2023-11-19 20:25:12,444 - harmonypy - INFO - sklearn.KMeans initialization complete.
INFO:harmonypy:sklearn.KMeans initialization complete.
2023-11-19 20:25:12,725 - harmonypy - INFO - Iteration 1 of 10
INFO:harmonypy:Iteration 1 of 10
2023-11-19 20:25:19,161 - harmonypy - INFO - Iteration 2 of 10
INFO:harmonypy:Iteration 2 of 10
2023-11-19 20:25:25,779 - harmonypy - INFO - Iteration 3 of 10
INFO:harmonypy:Iteration 3 of 10
2023-11-19 20:25:32,350 - harmonypy - INFO - Iteration 4 of 10
INFO:harmonypy:Iteration 4 of 10
2023-11-19 20:25:38,889 - harmonypy - INFO - Iteration 5 of 10
INFO:harmonypy:Iteration 5 of 10
2023-11-19 20:25:43,768 - harmonypy - INFO - Converged after 5 iterations
INFO:harmonypy:Converged after 5 iterations

Out[40]:

AnnData object with n_obs × n_vars = 26707 × 3000
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'nUMIs', 'mito_perc', 'detected_genes', 'cell_complexity', 'n_genes', 'doublet_score', 'predicted_doublet', 'passing_mt', 'passing_nUMIs', 'passing_ngenes', 'topic_0', 'topic_1', 'topic_2', 'topic_3', 'topic_4', 'topic_5', 'topic_6', 'topic_7', 'topic_8', 'topic_9', 'topic_10', 'topic_11', 'topic_12', 'topic_13', 'topic_14', 'LDA_cluster'
    var: 'feature_types', 'gene_id', 'mt', 'n_cells', 'percent_cells', 'robust', 'mean', 'var', 'residual_variances', 'highly_variable_rank', 'highly_variable_features'
    uns: 'scrublet', 'layers_counts', 'log1p', 'hvg', 'scaled|original|pca_var_ratios', 'scaled|original|cum_sum_eigenvalues', 'batch_colors', 'cell_type_colors', 'topic_dendogram'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap', 'scaled|original|X_pca', 'X_mde_pca', 'X_topic_compositions', 'X_umap_features', 'X_mde_mira', 'X_mde_mira_topic', 'X_mde_mira_feature', 'X_harmony'
    varm: 'scaled|original|pca_loadings', 'topic_feature_compositions', 'topic_feature_activations'
    layers: 'counts', 'scaled', 'lognorm'

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adata.obsm["X_mde_harmony"] = ov.utils.mde(adata.obsm["X_harmony"])
adata.obsm["X_mde_harmony"] = ov.utils.mde(adata.obsm["X_harmony"])

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ov.utils.embedding(adata,
                basis='X_mde_harmony',frameon='small',
                color=['batch','cell_type'],show=False)
ov.utils.embedding(adata,
                basis='X_mde_harmony',frameon='small',
                color=['batch','cell_type'],show=False)

Out[42]:

[<AxesSubplot: title={'center': 'batch'}, xlabel='X_mde_harmony1', ylabel='X_mde_harmony2'>,
 <AxesSubplot: title={'center': 'cell_type'}, xlabel='X_mde_harmony1', ylabel='X_mde_harmony2'>]

Combat¶

combat is a batch effect correction method that is very widely used in bulk RNA-seq, and it works just as well on single-cell sequencing data.

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adata_combat=ov.single.batch_correction(adata,batch_key='batch',
                                        methods='combat',n_pcs=50)
adata
adata_combat=ov.single.batch_correction(adata,batch_key='batch',
                                        methods='combat',n_pcs=50)
adata

...Begin using combat to correct batch effect
Standardizing Data across genes.

Found 3 batches

Found 0 numerical variables:
	

Fitting L/S model and finding priors

Finding parametric adjustments

Adjusting data

Out[43]:

AnnData object with n_obs × n_vars = 26707 × 3000
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'nUMIs', 'mito_perc', 'detected_genes', 'cell_complexity', 'n_genes', 'doublet_score', 'predicted_doublet', 'passing_mt', 'passing_nUMIs', 'passing_ngenes', 'topic_0', 'topic_1', 'topic_2', 'topic_3', 'topic_4', 'topic_5', 'topic_6', 'topic_7', 'topic_8', 'topic_9', 'topic_10', 'topic_11', 'topic_12', 'topic_13', 'topic_14', 'LDA_cluster'
    var: 'feature_types', 'gene_id', 'mt', 'n_cells', 'percent_cells', 'robust', 'mean', 'var', 'residual_variances', 'highly_variable_rank', 'highly_variable_features'
    uns: 'scrublet', 'layers_counts', 'log1p', 'hvg', 'scaled|original|pca_var_ratios', 'scaled|original|cum_sum_eigenvalues', 'batch_colors', 'cell_type_colors', 'topic_dendogram'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap', 'scaled|original|X_pca', 'X_mde_pca', 'X_topic_compositions', 'X_umap_features', 'X_mde_mira', 'X_mde_mira_topic', 'X_mde_mira_feature', 'X_harmony', 'X_mde_harmony', 'X_combat'
    varm: 'scaled|original|pca_loadings', 'topic_feature_compositions', 'topic_feature_activations'
    layers: 'counts', 'scaled', 'lognorm'

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adata.obsm["X_mde_combat"] = ov.utils.mde(adata.obsm["X_combat"])
adata.obsm["X_mde_combat"] = ov.utils.mde(adata.obsm["X_combat"])

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ov.utils.embedding(adata,
                basis='X_mde_combat',frameon='small',
                color=['batch','cell_type'],show=False)
ov.utils.embedding(adata,
                basis='X_mde_combat',frameon='small',
                color=['batch','cell_type'],show=False)

Out[45]:

[<AxesSubplot: title={'center': 'batch'}, xlabel='X_mde_combat1', ylabel='X_mde_combat2'>,
 <AxesSubplot: title={'center': 'cell_type'}, xlabel='X_mde_combat1', ylabel='X_mde_combat2'>]

scanorama¶

Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments, laboratories and technologies can uncover biological insights, but current methods for scRNA-seq data integration are limited by a requirement for datasets to derive from functionally similar cells. We present Scanorama, an algorithm that identifies and merges the shared cell types among all pairs of datasets and accurately integrates heterogeneous collections of scRNA-seq data.

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adata_scanorama=ov.single.batch_correction(adata,batch_key='batch',
                                        methods='scanorama',n_pcs=50)
adata
adata_scanorama=ov.single.batch_correction(adata,batch_key='batch',
                                        methods='scanorama',n_pcs=50)
adata

...Begin using scanorama to correct batch effect
s1d3
s2d1
s3d7
Found 3000 genes among all datasets
[[0.         0.50093205 0.5758346 ]
 [0.         0.         0.60733037]
 [0.         0.         0.        ]]
Processing datasets (1, 2)
Processing datasets (0, 2)
Processing datasets (0, 1)
(26707, 50)

Out[46]:

AnnData object with n_obs × n_vars = 26707 × 3000
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'nUMIs', 'mito_perc', 'detected_genes', 'cell_complexity', 'n_genes', 'doublet_score', 'predicted_doublet', 'passing_mt', 'passing_nUMIs', 'passing_ngenes', 'topic_0', 'topic_1', 'topic_2', 'topic_3', 'topic_4', 'topic_5', 'topic_6', 'topic_7', 'topic_8', 'topic_9', 'topic_10', 'topic_11', 'topic_12', 'topic_13', 'topic_14', 'LDA_cluster'
    var: 'feature_types', 'gene_id', 'mt', 'n_cells', 'percent_cells', 'robust', 'mean', 'var', 'residual_variances', 'highly_variable_rank', 'highly_variable_features'
    uns: 'scrublet', 'layers_counts', 'log1p', 'hvg', 'scaled|original|pca_var_ratios', 'scaled|original|cum_sum_eigenvalues', 'batch_colors', 'cell_type_colors', 'topic_dendogram'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap', 'scaled|original|X_pca', 'X_mde_pca', 'X_topic_compositions', 'X_umap_features', 'X_mde_mira', 'X_mde_mira_topic', 'X_mde_mira_feature', 'X_harmony', 'X_mde_harmony', 'X_combat', 'X_mde_combat', 'X_scanorama'
    varm: 'scaled|original|pca_loadings', 'topic_feature_compositions', 'topic_feature_activations'
    layers: 'counts', 'scaled', 'lognorm'

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adata.obsm["X_mde_scanorama"] = ov.utils.mde(adata.obsm["X_scanorama"])
adata.obsm["X_mde_scanorama"] = ov.utils.mde(adata.obsm["X_scanorama"])

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ov.utils.embedding(adata,
                basis='X_mde_scanorama',frameon='small',
                color=['batch','cell_type'],show=False)
ov.utils.embedding(adata,
                basis='X_mde_scanorama',frameon='small',
                color=['batch','cell_type'],show=False)

Out[48]:

[<AxesSubplot: title={'center': 'batch'}, xlabel='X_mde_scanorama1', ylabel='X_mde_scanorama2'>,
 <AxesSubplot: title={'center': 'cell_type'}, xlabel='X_mde_scanorama1', ylabel='X_mde_scanorama2'>]

scVI¶

An important task of single-cell analysis is the integration of several samples, which we can perform with scVI. For integration, scVI treats the data as unlabelled. When our dataset is fully labelled (perhaps in independent studies, or independent analysis pipelines), we can obtain an integration that better preserves biology using scANVI, which incorporates cell type annotation information. Here we demonstrate this functionality with an integrated analysis of cells from the lung atlas integration task from the scIB manuscript. The same pipeline would generally be used to analyze any collection of scRNA-seq datasets.

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adata_scvi=ov.single.batch_correction(adata,batch_key='batch',
                           methods='scVI',n_layers=2, n_latent=30, gene_likelihood="nb")
adata
adata_scvi=ov.single.batch_correction(adata,batch_key='batch',
                           methods='scVI',n_layers=2, n_latent=30, gene_likelihood="nb")
adata

...Begin using scVI to correct batch effect

Global seed set to 0
No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

Epoch 300/300: 100%|██████████| 300/300 [05:51<00:00,  1.18s/it, loss=1.51e+03, v_num=1]

`Trainer.fit` stopped: `max_epochs=300` reached.

Epoch 300/300: 100%|██████████| 300/300 [05:51<00:00,  1.17s/it, loss=1.51e+03, v_num=1]

Out[3]:

AnnData object with n_obs × n_vars = 26707 × 3000
    obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'nUMIs', 'mito_perc', 'detected_genes', 'cell_complexity', 'n_genes', 'doublet_score', 'predicted_doublet', 'passing_mt', 'passing_nUMIs', 'passing_ngenes', 'topic_0', 'topic_1', 'topic_2', 'topic_3', 'topic_4', 'topic_5', 'topic_6', 'topic_7', 'topic_8', 'topic_9', 'topic_10', 'topic_11', 'topic_12', 'topic_13', 'topic_14', 'LDA_cluster', '_scvi_batch', '_scvi_labels'
    var: 'feature_types', 'gene_id', 'mt', 'n_cells', 'percent_cells', 'robust', 'mean', 'var', 'residual_variances', 'highly_variable_rank', 'highly_variable_features'
    uns: 'batch_colors', 'cell_type_colors', 'hvg', 'layers_counts', 'log1p', 'scaled|original|cum_sum_eigenvalues', 'scaled|original|pca_var_ratios', 'scrublet', 'topic_dendogram', '_scvi_uuid', '_scvi_manager_uuid'
    obsm: 'ADT_X_pca', 'ADT_X_umap', 'ADT_isotype_controls', 'GEX_X_pca', 'GEX_X_umap', 'X_combat', 'X_harmony', 'X_mde_combat', 'X_mde_harmony', 'X_mde_mira', 'X_mde_mira_feature', 'X_mde_mira_topic', 'X_mde_pca', 'X_mde_scanorama', 'X_scanorama', 'X_topic_compositions', 'X_umap_features', 'scaled|original|X_pca', 'X_scVI'
    varm: 'scaled|original|pca_loadings', 'topic_feature_activations', 'topic_feature_compositions'
    layers: 'counts', 'lognorm', 'scaled'

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adata.obsm["X_mde_scVI"] = ov.utils.mde(adata.obsm["X_scVI"])
adata.obsm["X_mde_scVI"] = ov.utils.mde(adata.obsm["X_scVI"])

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ov.utils.embedding(adata,
                basis='X_mde_scVI',frameon='small',
                color=['batch','cell_type'],show=False)
ov.utils.embedding(adata,
                basis='X_mde_scVI',frameon='small',
                color=['batch','cell_type'],show=False)

Out[5]:

[<AxesSubplot: title={'center': 'batch'}, xlabel='X_mde_scVI1', ylabel='X_mde_scVI2'>,
 <AxesSubplot: title={'center': 'cell_type'}, xlabel='X_mde_scVI1', ylabel='X_mde_scVI2'>]

MIRA+CODAL¶

Topic modeling of batched single-cell data is challenging because these models cannot typically distinguish between biological and technical effects of the assay. CODAL (COvariate Disentangling Augmented Loss) uses a novel mutual information regularization technique to explicitly disentangle these two sources of variation.

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LDA_obj=ov.utils.LDA_topic(adata,feature_type='expression',
                  highly_variable_key='highly_variable_features',
                 layers='counts',batch_key='batch',learning_rate=1e-3)
LDA_obj=ov.utils.LDA_topic(adata,feature_type='expression',
                  highly_variable_key='highly_variable_features',
                 layers='counts',batch_key='batch',learning_rate=1e-3)

INFO:mira.adata_interface.topic_model:Predicting expression from genes from col: highly_variable_features

mira have been install version: 2.1.0

Gathering dataset statistics:   0%|          | 0/26707 [00:00<?, ?it/s]

Learning rate range test:   0%|          | 0/98 [00:00<?, ?it/s]

INFO:mira.topic_model.base:Set learning rates to: (0.0061957597093704065, 0.22248668375233174)

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LDA_obj.plot_topic_contributions(6)
LDA_obj.plot_topic_contributions(6)

Gathering dataset statistics:   0%|          | 0/26707 [00:00<?, ?it/s]

Epoch 0:   0%|          | 0/24 [00:00<?, ?it/s]

Predicting latent vars:   0%|          | 0/105 [00:00<?, ?it/s]

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LDA_obj.predicted(15)
LDA_obj.predicted(15)

INFO:mira.adata_interface.topic_model:Predicting expression from genes from col: highly_variable_features

running LDA topic predicted

Gathering dataset statistics:   0%|          | 0/26707 [00:00<?, ?it/s]

Epoch 0:   0%|          | 0/24 [00:00<?, ?it/s]

INFO:mira.topic_model.base:Moving model to device: cpu

Predicting latent vars:   0%|          | 0/105 [00:00<?, ?it/s]

INFO:mira.adata_interface.core:Added key to obsm: X_topic_compositions
INFO:mira.adata_interface.core:Added key to obsm: X_umap_features
INFO:mira.adata_interface.topic_model:Added cols: topic_0, topic_1, topic_2, topic_3, topic_4, topic_5, topic_6, topic_7, topic_8, topic_9, topic_10, topic_11, topic_12, topic_13, topic_14
INFO:mira.adata_interface.core:Added key to varm: topic_feature_compositions
INFO:mira.adata_interface.core:Added key to varm: topic_feature_activations
INFO:mira.adata_interface.topic_model:Added key to uns: topic_dendogram

finished: found 15 clusters and added
    'LDA_cluster', the cluster labels (adata.obs, categorical)

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adata.obsm["X_mde_mira_topic"] = ov.utils.mde(adata.obsm["X_topic_compositions"])
adata.obsm["X_mde_mira_feature"] = ov.utils.mde(adata.obsm["X_umap_features"])
adata.obsm["X_mde_mira_topic"] = ov.utils.mde(adata.obsm["X_topic_compositions"])
adata.obsm["X_mde_mira_feature"] = ov.utils.mde(adata.obsm["X_umap_features"])

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ov.utils.embedding(adata,
                basis='X_mde_mira_topic',frameon='small',
                color=['batch','cell_type'],show=False)
ov.utils.embedding(adata,
                basis='X_mde_mira_topic',frameon='small',
                color=['batch','cell_type'],show=False)

Out[38]:

[<AxesSubplot: title={'center': 'batch'}, xlabel='X_mde_mira_topic1', ylabel='X_mde_mira_topic2'>,
 <AxesSubplot: title={'center': 'cell_type'}, xlabel='X_mde_mira_topic1', ylabel='X_mde_mira_topic2'>]

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ov.utils.embedding(adata,
                basis='X_mde_mira_feature',frameon='small',
                color=['batch','cell_type'],show=False)
ov.utils.embedding(adata,
                basis='X_mde_mira_feature',frameon='small',
                color=['batch','cell_type'],show=False)

Out[39]:

[<AxesSubplot: title={'center': 'batch'}, xlabel='X_mde_mira_feature1', ylabel='X_mde_mira_feature2'>,
 <AxesSubplot: title={'center': 'cell_type'}, xlabel='X_mde_mira_feature1', ylabel='X_mde_mira_feature2'>]

Benchmarking test¶

The methods demonstrated here are selected based on results from benchmarking experiments including the single-cell integration benchmarking project [Luecken et al., 2021]. This project also produced a software package called scib that can be used to run a range of integration methods as well as the metrics that were used for evaluation. In this section, we show how to use this package to evaluate the quality of an integration.

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adata.write_h5ad('neurips2021_batch_all.h5ad',compression='gzip')
adata.write_h5ad('neurips2021_batch_all.h5ad',compression='gzip')

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adata=sc.read('neurips2021_batch_all.h5ad')
adata=sc.read('neurips2021_batch_all.h5ad')

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adata.obsm['X_pca']=adata.obsm['scaled|original|X_pca'].copy()
adata.obsm['X_mira_topic']=adata.obsm['X_topic_compositions'].copy()
adata.obsm['X_mira_feature']=adata.obsm['X_umap_features'].copy()
adata.obsm['X_pca']=adata.obsm['scaled|original|X_pca'].copy()
adata.obsm['X_mira_topic']=adata.obsm['X_topic_compositions'].copy()
adata.obsm['X_mira_feature']=adata.obsm['X_umap_features'].copy()

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from scib_metrics.benchmark import Benchmarker
bm = Benchmarker(
    adata,
    batch_key="batch",
    label_key="cell_type",
    embedding_obsm_keys=["X_pca", "X_combat", "X_harmony",
                         'X_scanorama','X_mira_topic','X_mira_feature','X_scVI'],
    n_jobs=8,
)
bm.benchmark()
from scib_metrics.benchmark import Benchmarker
bm = Benchmarker(
    adata,
    batch_key="batch",
    label_key="cell_type",
    embedding_obsm_keys=["X_pca", "X_combat", "X_harmony",
                         'X_scanorama','X_mira_topic','X_mira_feature','X_scVI'],
    n_jobs=8,
)
bm.benchmark()

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bm.plot_results_table(min_max_scale=False)
bm.plot_results_table(min_max_scale=False)

Out[9]:

<plottable.table.Table at 0x7f0d9a429d60>

We can find that harmony removes the batch effect the best of the three methods that do not use the GPU, scVI is method to remove batch effect using GPU.